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E-commerce Data Cleaning & Quality Analysis
Performed a data cleaning and quality analysis project using an e-commerce dataset containing approximately 10,000 records.
The objective was to improve data consistency, identify data quality issues, and prepare reliable data for further business analysis.
Key Activities:
Data Quality Assessment
Identifying and Handling Missing Values
Detecting Inconsistent and Invalid Records
Data Standardization and Transformation
Business Rule Validation
Identifying Potential Revenue and Shipping Issues
Key Findings:
Identified 243 orders where shipping costs exceeded the applied discounts, highlighting potential areas for further operational investigation.
Tools & Technologies:
Python, Pandas, Data Cleaning, Data Quality Analysis, Jupyter Notebook.
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